Detection and veracity analysis of fake news via scrapping and authenticating the web search
Social media has become a part of our day-to-day life and has become one of the significant sources of information. Most of the information available on social media is in the form of images. This has given rise to fake news event distribution, which is misinforming the users. Hence, to tackle this...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Vishwakarma, Dinesh Kumar [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2019transfer abstract |
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| Schlagwörter: |
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| Umfang: |
13 |
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| Übergeordnetes Werk: |
Enthalten in: The Sulfur Microbial Diet and Micro-managing Early-Onset Colorectal Neoplasia - Billings, Wade ELSEVIER, 2021, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:58 ; year:2019 ; pages:217-229 ; extent:13 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.cogsys.2019.07.004 |
|---|
| Katalog-ID: |
ELV04803410X |
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| 520 | |a Social media has become a part of our day-to-day life and has become one of the significant sources of information. Most of the information available on social media is in the form of images. This has given rise to fake news event distribution, which is misinforming the users. Hence, to tackle this problem, we propose a model which is concerned with the veracity analysis of information on various social media platforms available in the form of images. It involves an algorithm which validates the veracity of image text by exploring it on web and then checking the credibility of the top 15 Google search results by subsequently calculating the reality parameter (Rp), which if exceeds a threshold value, an event is classified as real else fake. In order to test the performance of our proposed approach, we compute the recognition accuracy, and the highest accuracy is compared with similar state-of-the-art models to demonstrate the superior performance of our approach. | ||
| 520 | |a Social media has become a part of our day-to-day life and has become one of the significant sources of information. Most of the information available on social media is in the form of images. This has given rise to fake news event distribution, which is misinforming the users. Hence, to tackle this problem, we propose a model which is concerned with the veracity analysis of information on various social media platforms available in the form of images. It involves an algorithm which validates the veracity of image text by exploring it on web and then checking the credibility of the top 15 Google search results by subsequently calculating the reality parameter (Rp), which if exceeds a threshold value, an event is classified as real else fake. In order to test the performance of our proposed approach, we compute the recognition accuracy, and the highest accuracy is compared with similar state-of-the-art models to demonstrate the superior performance of our approach. | ||
| 650 | 7 | |a Veracity |2 Elsevier | |
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| 650 | 7 | |a Detection of fake news |2 Elsevier | |
| 650 | 7 | |a Fake news in images |2 Elsevier | |
| 650 | 7 | |a Fake news analysis |2 Elsevier | |
| 700 | 1 | |a Varshney, Deepika |4 oth | |
| 700 | 1 | |a Yadav, Ashima |4 oth | |
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10.1016/j.cogsys.2019.07.004 doi GBV00000000000775.pica (DE-627)ELV04803410X (ELSEVIER)S1389-0417(19)30102-0 DE-627 ger DE-627 rakwb eng 610 VZ 44.87 bkl Vishwakarma, Dinesh Kumar verfasserin aut Detection and veracity analysis of fake news via scrapping and authenticating the web search 2019transfer abstract 13 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Social media has become a part of our day-to-day life and has become one of the significant sources of information. Most of the information available on social media is in the form of images. This has given rise to fake news event distribution, which is misinforming the users. Hence, to tackle this problem, we propose a model which is concerned with the veracity analysis of information on various social media platforms available in the form of images. It involves an algorithm which validates the veracity of image text by exploring it on web and then checking the credibility of the top 15 Google search results by subsequently calculating the reality parameter (Rp), which if exceeds a threshold value, an event is classified as real else fake. In order to test the performance of our proposed approach, we compute the recognition accuracy, and the highest accuracy is compared with similar state-of-the-art models to demonstrate the superior performance of our approach. Social media has become a part of our day-to-day life and has become one of the significant sources of information. Most of the information available on social media is in the form of images. This has given rise to fake news event distribution, which is misinforming the users. Hence, to tackle this problem, we propose a model which is concerned with the veracity analysis of information on various social media platforms available in the form of images. It involves an algorithm which validates the veracity of image text by exploring it on web and then checking the credibility of the top 15 Google search results by subsequently calculating the reality parameter (Rp), which if exceeds a threshold value, an event is classified as real else fake. In order to test the performance of our proposed approach, we compute the recognition accuracy, and the highest accuracy is compared with similar state-of-the-art models to demonstrate the superior performance of our approach. Veracity Elsevier Social media Elsevier Detection of fake news Elsevier Fake news in images Elsevier Fake news analysis Elsevier Varshney, Deepika oth Yadav, Ashima oth Enthalten in Elsevier Science Billings, Wade ELSEVIER The Sulfur Microbial Diet and Micro-managing Early-Onset Colorectal Neoplasia 2021 Amsterdam [u.a.] (DE-627)ELV006801218 volume:58 year:2019 pages:217-229 extent:13 https://doi.org/10.1016/j.cogsys.2019.07.004 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.87 Gastroenterologie VZ AR 58 2019 217-229 13 |
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10.1016/j.cogsys.2019.07.004 doi GBV00000000000775.pica (DE-627)ELV04803410X (ELSEVIER)S1389-0417(19)30102-0 DE-627 ger DE-627 rakwb eng 610 VZ 44.87 bkl Vishwakarma, Dinesh Kumar verfasserin aut Detection and veracity analysis of fake news via scrapping and authenticating the web search 2019transfer abstract 13 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Social media has become a part of our day-to-day life and has become one of the significant sources of information. Most of the information available on social media is in the form of images. This has given rise to fake news event distribution, which is misinforming the users. Hence, to tackle this problem, we propose a model which is concerned with the veracity analysis of information on various social media platforms available in the form of images. It involves an algorithm which validates the veracity of image text by exploring it on web and then checking the credibility of the top 15 Google search results by subsequently calculating the reality parameter (Rp), which if exceeds a threshold value, an event is classified as real else fake. In order to test the performance of our proposed approach, we compute the recognition accuracy, and the highest accuracy is compared with similar state-of-the-art models to demonstrate the superior performance of our approach. Social media has become a part of our day-to-day life and has become one of the significant sources of information. Most of the information available on social media is in the form of images. This has given rise to fake news event distribution, which is misinforming the users. Hence, to tackle this problem, we propose a model which is concerned with the veracity analysis of information on various social media platforms available in the form of images. It involves an algorithm which validates the veracity of image text by exploring it on web and then checking the credibility of the top 15 Google search results by subsequently calculating the reality parameter (Rp), which if exceeds a threshold value, an event is classified as real else fake. In order to test the performance of our proposed approach, we compute the recognition accuracy, and the highest accuracy is compared with similar state-of-the-art models to demonstrate the superior performance of our approach. Veracity Elsevier Social media Elsevier Detection of fake news Elsevier Fake news in images Elsevier Fake news analysis Elsevier Varshney, Deepika oth Yadav, Ashima oth Enthalten in Elsevier Science Billings, Wade ELSEVIER The Sulfur Microbial Diet and Micro-managing Early-Onset Colorectal Neoplasia 2021 Amsterdam [u.a.] (DE-627)ELV006801218 volume:58 year:2019 pages:217-229 extent:13 https://doi.org/10.1016/j.cogsys.2019.07.004 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.87 Gastroenterologie VZ AR 58 2019 217-229 13 |
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10.1016/j.cogsys.2019.07.004 doi GBV00000000000775.pica (DE-627)ELV04803410X (ELSEVIER)S1389-0417(19)30102-0 DE-627 ger DE-627 rakwb eng 610 VZ 44.87 bkl Vishwakarma, Dinesh Kumar verfasserin aut Detection and veracity analysis of fake news via scrapping and authenticating the web search 2019transfer abstract 13 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Social media has become a part of our day-to-day life and has become one of the significant sources of information. Most of the information available on social media is in the form of images. This has given rise to fake news event distribution, which is misinforming the users. Hence, to tackle this problem, we propose a model which is concerned with the veracity analysis of information on various social media platforms available in the form of images. It involves an algorithm which validates the veracity of image text by exploring it on web and then checking the credibility of the top 15 Google search results by subsequently calculating the reality parameter (Rp), which if exceeds a threshold value, an event is classified as real else fake. In order to test the performance of our proposed approach, we compute the recognition accuracy, and the highest accuracy is compared with similar state-of-the-art models to demonstrate the superior performance of our approach. Social media has become a part of our day-to-day life and has become one of the significant sources of information. Most of the information available on social media is in the form of images. This has given rise to fake news event distribution, which is misinforming the users. Hence, to tackle this problem, we propose a model which is concerned with the veracity analysis of information on various social media platforms available in the form of images. It involves an algorithm which validates the veracity of image text by exploring it on web and then checking the credibility of the top 15 Google search results by subsequently calculating the reality parameter (Rp), which if exceeds a threshold value, an event is classified as real else fake. In order to test the performance of our proposed approach, we compute the recognition accuracy, and the highest accuracy is compared with similar state-of-the-art models to demonstrate the superior performance of our approach. Veracity Elsevier Social media Elsevier Detection of fake news Elsevier Fake news in images Elsevier Fake news analysis Elsevier Varshney, Deepika oth Yadav, Ashima oth Enthalten in Elsevier Science Billings, Wade ELSEVIER The Sulfur Microbial Diet and Micro-managing Early-Onset Colorectal Neoplasia 2021 Amsterdam [u.a.] (DE-627)ELV006801218 volume:58 year:2019 pages:217-229 extent:13 https://doi.org/10.1016/j.cogsys.2019.07.004 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.87 Gastroenterologie VZ AR 58 2019 217-229 13 |
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10.1016/j.cogsys.2019.07.004 doi GBV00000000000775.pica (DE-627)ELV04803410X (ELSEVIER)S1389-0417(19)30102-0 DE-627 ger DE-627 rakwb eng 610 VZ 44.87 bkl Vishwakarma, Dinesh Kumar verfasserin aut Detection and veracity analysis of fake news via scrapping and authenticating the web search 2019transfer abstract 13 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Social media has become a part of our day-to-day life and has become one of the significant sources of information. Most of the information available on social media is in the form of images. This has given rise to fake news event distribution, which is misinforming the users. Hence, to tackle this problem, we propose a model which is concerned with the veracity analysis of information on various social media platforms available in the form of images. It involves an algorithm which validates the veracity of image text by exploring it on web and then checking the credibility of the top 15 Google search results by subsequently calculating the reality parameter (Rp), which if exceeds a threshold value, an event is classified as real else fake. In order to test the performance of our proposed approach, we compute the recognition accuracy, and the highest accuracy is compared with similar state-of-the-art models to demonstrate the superior performance of our approach. Social media has become a part of our day-to-day life and has become one of the significant sources of information. Most of the information available on social media is in the form of images. This has given rise to fake news event distribution, which is misinforming the users. Hence, to tackle this problem, we propose a model which is concerned with the veracity analysis of information on various social media platforms available in the form of images. It involves an algorithm which validates the veracity of image text by exploring it on web and then checking the credibility of the top 15 Google search results by subsequently calculating the reality parameter (Rp), which if exceeds a threshold value, an event is classified as real else fake. In order to test the performance of our proposed approach, we compute the recognition accuracy, and the highest accuracy is compared with similar state-of-the-art models to demonstrate the superior performance of our approach. Veracity Elsevier Social media Elsevier Detection of fake news Elsevier Fake news in images Elsevier Fake news analysis Elsevier Varshney, Deepika oth Yadav, Ashima oth Enthalten in Elsevier Science Billings, Wade ELSEVIER The Sulfur Microbial Diet and Micro-managing Early-Onset Colorectal Neoplasia 2021 Amsterdam [u.a.] (DE-627)ELV006801218 volume:58 year:2019 pages:217-229 extent:13 https://doi.org/10.1016/j.cogsys.2019.07.004 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.87 Gastroenterologie VZ AR 58 2019 217-229 13 |
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10.1016/j.cogsys.2019.07.004 doi GBV00000000000775.pica (DE-627)ELV04803410X (ELSEVIER)S1389-0417(19)30102-0 DE-627 ger DE-627 rakwb eng 610 VZ 44.87 bkl Vishwakarma, Dinesh Kumar verfasserin aut Detection and veracity analysis of fake news via scrapping and authenticating the web search 2019transfer abstract 13 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Social media has become a part of our day-to-day life and has become one of the significant sources of information. Most of the information available on social media is in the form of images. This has given rise to fake news event distribution, which is misinforming the users. Hence, to tackle this problem, we propose a model which is concerned with the veracity analysis of information on various social media platforms available in the form of images. It involves an algorithm which validates the veracity of image text by exploring it on web and then checking the credibility of the top 15 Google search results by subsequently calculating the reality parameter (Rp), which if exceeds a threshold value, an event is classified as real else fake. In order to test the performance of our proposed approach, we compute the recognition accuracy, and the highest accuracy is compared with similar state-of-the-art models to demonstrate the superior performance of our approach. Social media has become a part of our day-to-day life and has become one of the significant sources of information. Most of the information available on social media is in the form of images. This has given rise to fake news event distribution, which is misinforming the users. Hence, to tackle this problem, we propose a model which is concerned with the veracity analysis of information on various social media platforms available in the form of images. It involves an algorithm which validates the veracity of image text by exploring it on web and then checking the credibility of the top 15 Google search results by subsequently calculating the reality parameter (Rp), which if exceeds a threshold value, an event is classified as real else fake. In order to test the performance of our proposed approach, we compute the recognition accuracy, and the highest accuracy is compared with similar state-of-the-art models to demonstrate the superior performance of our approach. Veracity Elsevier Social media Elsevier Detection of fake news Elsevier Fake news in images Elsevier Fake news analysis Elsevier Varshney, Deepika oth Yadav, Ashima oth Enthalten in Elsevier Science Billings, Wade ELSEVIER The Sulfur Microbial Diet and Micro-managing Early-Onset Colorectal Neoplasia 2021 Amsterdam [u.a.] (DE-627)ELV006801218 volume:58 year:2019 pages:217-229 extent:13 https://doi.org/10.1016/j.cogsys.2019.07.004 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.87 Gastroenterologie VZ AR 58 2019 217-229 13 |
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The Sulfur Microbial Diet and Micro-managing Early-Onset Colorectal Neoplasia |
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The Sulfur Microbial Diet and Micro-managing Early-Onset Colorectal Neoplasia |
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Detection and veracity analysis of fake news via scrapping and authenticating the web search |
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Detection and veracity analysis of fake news via scrapping and authenticating the web search |
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Vishwakarma, Dinesh Kumar |
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The Sulfur Microbial Diet and Micro-managing Early-Onset Colorectal Neoplasia |
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10.1016/j.cogsys.2019.07.004 |
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detection and veracity analysis of fake news via scrapping and authenticating the web search |
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Detection and veracity analysis of fake news via scrapping and authenticating the web search |
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Social media has become a part of our day-to-day life and has become one of the significant sources of information. Most of the information available on social media is in the form of images. This has given rise to fake news event distribution, which is misinforming the users. Hence, to tackle this problem, we propose a model which is concerned with the veracity analysis of information on various social media platforms available in the form of images. It involves an algorithm which validates the veracity of image text by exploring it on web and then checking the credibility of the top 15 Google search results by subsequently calculating the reality parameter (Rp), which if exceeds a threshold value, an event is classified as real else fake. In order to test the performance of our proposed approach, we compute the recognition accuracy, and the highest accuracy is compared with similar state-of-the-art models to demonstrate the superior performance of our approach. |
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Social media has become a part of our day-to-day life and has become one of the significant sources of information. Most of the information available on social media is in the form of images. This has given rise to fake news event distribution, which is misinforming the users. Hence, to tackle this problem, we propose a model which is concerned with the veracity analysis of information on various social media platforms available in the form of images. It involves an algorithm which validates the veracity of image text by exploring it on web and then checking the credibility of the top 15 Google search results by subsequently calculating the reality parameter (Rp), which if exceeds a threshold value, an event is classified as real else fake. In order to test the performance of our proposed approach, we compute the recognition accuracy, and the highest accuracy is compared with similar state-of-the-art models to demonstrate the superior performance of our approach. |
| abstract_unstemmed |
Social media has become a part of our day-to-day life and has become one of the significant sources of information. Most of the information available on social media is in the form of images. This has given rise to fake news event distribution, which is misinforming the users. Hence, to tackle this problem, we propose a model which is concerned with the veracity analysis of information on various social media platforms available in the form of images. It involves an algorithm which validates the veracity of image text by exploring it on web and then checking the credibility of the top 15 Google search results by subsequently calculating the reality parameter (Rp), which if exceeds a threshold value, an event is classified as real else fake. In order to test the performance of our proposed approach, we compute the recognition accuracy, and the highest accuracy is compared with similar state-of-the-art models to demonstrate the superior performance of our approach. |
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Detection and veracity analysis of fake news via scrapping and authenticating the web search |
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https://doi.org/10.1016/j.cogsys.2019.07.004 |
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Varshney, Deepika Yadav, Ashima |
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